Machine learning for predicting locations of objects perceived by autonomous vehicles
US10579063B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 23, 2017 |
| Grant date | Mar 3, 2020 |
| Priority date | — |
| Expiry date | Aug 23, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG08G1/166
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
The present disclosure provides systems and methods for predicting the future locations of objects that are perceived by autonomous vehicles. An autonomous vehicle can include a prediction system that, for each object perceived by the autonomous vehicle, generates one or more potential goals, selects one or more of the potential goals, and develops one or more trajectories by which the object can achieve the one or more selected goals. The prediction systems and methods described herein can include or leverage one or more machine-learned models that assist in predicting the future locations of the objects. As an example, in some implementations, the prediction system can include a machine-learned static object classifier, a machine-learned goal scoring model, a machine-learned trajectory development model, a machine-learned ballistic quality classifier, and/or other machine-learned models. The use of machine-learned models can improve the speed, quality, and/or accuracy of the generated predictions.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.